scholarly journals Rotordynamic Faults: Recent Advances in Diagnosis and Prognosis

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Ryan Walker ◽  
Sureshkumar Perinpanayagam ◽  
Ian K. Jennions
Keyword(s):  
Condition Monitoring ◽  
Health Management ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Bearing Failure ◽  
Rotating Machines ◽  
Diagnosis And Prognosis ◽  
Recent Developments ◽  
Identification Techniques ◽  
Intense Research

Diagnosis and condition monitoring in rotating machinery has been a subject of intense research for the last century. Recent developments indicate the drive towards integration of diagnosis and prognosis algorithms in future integrated vehicle health management (IVHM) systems. With this in mind, this paper concentrates on highlighting some of the latest research on common faults in rotating machines. Eight key faults have been described; the selected faults include unbalance, misalignment, rub/looseness, fluid-induced instability, bearing failure, shaft cracks, blade cracks, and shaft bow. Each of these faults has been detailed with regard to sensors, fault identification techniques, localization, prognosis, and modeling. The intent of the paper is to highlight the latest technologies pioneering the drive towards next-generation IVHM systems for rotating machinery.

Diagnosis of Local Fault and Identification of Transient Fault Force in Rotating Machinery

2005 ◽  
Vol 293-294 ◽  
pp. 475-482 ◽  
Author(s):  
Hong Liang Yao ◽  
Tao Yu ◽  
Xiao Peng Li ◽  
Qing Kai Han ◽  
Bang Chun Wen
Keyword(s):  
Numerical Simulations ◽  
Least Squares ◽  
Fault Location ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Residual Vibration ◽  
Transient Fault ◽  
Least Squares Fitting ◽  
Simple Matrix ◽  
Identification Techniques

Model based fault identification techniques can be used to diagnose local faults in rotating machinery; the equivalent loads that represent fault forces can be used to identify the fault location. But in some cases the equivalent loads smeared over many nodes, and make it hard to identify accurate fault location and transient fault force. In this paper, the fault location is identified using least squares fitting approach by the system’s vibration shape when the fault signal is periodic or quasi-periodic. And after the fault location is ascertained, the transient fault forces can be identified by transient residual vibration using simple matrix multiplications and additions. Numerical simulations and experiment on rotor to stator rub are used to test the method, which proved the efficiency of the method.

Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition

2017 ◽  
Vol 17 (3) ◽  
pp. 532-548 ◽  
Author(s):  
Mohamed Elforjani
Keyword(s):  
Acoustic Emission ◽  
Health Management ◽  
Processing Technique ◽  
Remaining Useful Life ◽  
Slow Speed ◽  
Signal Processing Technique ◽  
Monitoring And Diagnosis ◽  
Diagnosis And Prognosis ◽  
Recent Developments ◽  
Machine Health

The monitoring and diagnosis of rolling element bearings with acoustic emission and vibration measurements has evolved as one of the much used techniques for condition monitoring and diagnosis of rotating machinery. Furthermore, recent developments indicate the drive toward integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this article is an experimental study of slow speed bearings in a starved lubricated contact. It investigates the influence of grease starvation conditions on detection and monitoring natural defect initiation and propagation using acoustic emission approach. The experiments are also aimed at a comparison of results acquired by acoustic emission and vibration diagnosis on full-scale axial bearing. In addition to this, the article concentrates on the estimation of the remaining useful life for bearings while in operation. To implement this, a multilayer artificial neural network model has been proposed to correlate the selected acoustic emission features with corresponding bearing wear throughout laboratory experiments. Experiments confirm that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.

Localizing Unbalance Faults in Rotating Machinery

2013 ◽  
Author(s):  
R. B. Walker ◽  
S. Perinpanayagam ◽  
I. K. Jennions
Keyword(s):  
Frequency Domain ◽  
Health Management ◽  
Sensor Placement ◽  
Rotating Machinery ◽  
Next Generation ◽  
Rotating Machines ◽  
Linear Features ◽  
Rotating Machine ◽  
Rules Of Thumb ◽  
Non Linear

Excessive levels of unbalance in rotating machinery continue to contribute to machine downtime and unscheduled and costly maintenance actions. Whilst unbalance as a rotordynamic fault has been studied in great detail during the last century, the localization of unbalance within a complex rotating machine is today often performed in practice using little more than ‘rules of thumb’. In this work, localizing excessive unbalance has been studied from an experimental perspective through the use of two rotordynamic test rigs fitted with multiple disks. Sub-synchronous non-linear features in the frequency domain have been identified and studied as a method of aiding the localization of unbalance faults, particularly in situations where sensor placement options are limited. The results of the study are discussed from the perspective of next-generation Integrated Vehicle Health Management (IVHM) systems for rotating machines.

Effects of the Shaft Normal Modes on the Model-Based Identification of Unbalances in Rotating Machines

2011 ◽  
Author(s):  
A. Vania ◽  
P. Pennacchi ◽  
S. Chatterton
Keyword(s):  
Normal Modes ◽  
Fault Identification ◽  
Diagnostic Strategy ◽  
Rotating Machines ◽  
Large Power ◽  
Identification Methods ◽  
Model Based ◽  
Rotating Machine ◽  
Experimental Response ◽  
Innovative Techniques

Model-based methods can be applied to identify the most likely faults that cause the experimental response of a rotating machine. Sometimes, the objective function, to be minimized in the fault identification method, shows multiple sufficiently low values that are associated with different sets of the equivalent excitations by means of which the fault can be modeled. In these cases, the knowledge of the contribution of each normal mode of interest to the vibration predicted at each measurement point can provide useful information to identify the actual fault. In this paper, the capabilities of an original diagnostic strategy that combines the use of common fault identification methods with innovative techniques based on a modal representation of the dynamic behavior of rotating machines is shown. This investigation approach has been successfully validated by means of the analysis of the abnormal vibrations of a large power unit.

scholarly journals Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Aisong Qin ◽  
Qinghua Zhang ◽  
Qin Hu ◽  
Guoxi Sun ◽  
Jun He ◽  
...  
Keyword(s):  
Health Management ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Programming Algorithm ◽  
Wiener Model ◽  
New Approach ◽  
Degradation Modeling ◽  
Proposed Model ◽  
Useful Life ◽  
Degradation Indicator

Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predicting the RUL of rotating machinery based on an optimal degradation indictor. First, a genetic programming algorithm is proposed to construct an optimal degradation indicator using the concept of FPT. Then, a Wiener model based on the obtained optimal degradation indicator is proposed, in which the sensitivities of the dimensionless parameters are utilized to determine the FPT. Finally, the expectation of the predicted RUL is calculated based on the proposed model, and the estimated mean degradation path is explicitly derived. To demonstrate the validity of this model, several experiments on RUL prediction are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of RUL prediction.

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